Computer-based information gathering, handling, manipulation, storage, and transmission have fostered the growth and acceptance of computation systems based upon adaptive learning with various neural network architectures. These computation systems are commonly called automatic learning networks, neural networks, hierarchically layered networks, and massively parallel computation networks. Applications of the computational systems represent potentially efficient approaches to solving problems such as providing automatic recognition, analysis and classification of character patterns in a particular image. In measuring the value of such applied systems, it is necessary to focus on two key operational parameters relative to conventional approaches. The parameters are speed and accuracy.
Accuracy of the systems has been improved steadily by employing more complex architectures, more extensive training routines, and multi-valued intermediate decision levels (e.g., gray scale encoding). Unfortunately, improvements to obtain greater system accuracy tend to adversely impact the speed of the system.
Since most systems are based on implementations using a general or special-purpose processor, complexity and rigorousness of such an implementation is generally translatable into additional program steps for the processor. In turn, the response of the processor is slowed by an amount commensurate with the additional number of program steps to be performed. As a result, faster, more effective neural network computation systems appear realizable only by replacement of the processor with a faster and equally, or more, accurate processor.